GRAPHICAL ABSTRACT

INTRODUCTION

Depression is a prevalent and debilitating mental health disorder characterized by persistent low mood, loss of interest in daily activities, low self-esteem, and cognitive and physical symptoms. It affects individuals of all ages and sexes, significantly diminishing quality of life and placing substantial burdens on global public health systems. According to the World Health Organization (WHO), depression is a leading cause of disability worldwide, affecting individuals across various socioeconomic statuses. Individuals with past emotional trauma, abuse, grievous loss, or other stressful events have a higher risk of developing depression. Females face a higher risk of depression than males [1].

The WHO defines anemia as a hemoglobin level below 13 g/dL in male and 12 g/dL in female, resulting in insufficient oxygen-carrying capacity to meet the body’s requirements and causing symptoms such as tiredness, weakness, and mental sluggishness [2]. India’s National Family Health Survey-5 reports a high prevalence of anemia, particularly among women of reproductive age and individuals from lower socioeconomic backgrounds [3].

Recent studies suggest a bidirectional relationship between anemia and depression. Anemic states may precipitate depressive symptoms through reduced cerebral oxygenation and altered neurotransmitter metabolism, while depression can lead to poor dietary intake and malabsorption, further exacerbating anemia. Studies conducted in China and France have shown a significant association between these conditions, highlighting the need for further investigation in diverse socio-cultural contexts [4,5].

Epidemiological studies have corroborated this link in various settings, including older adults in northern Iran [6], outpatient attendees of a rural hospital in Delhi [7], and participants in the Chennai Urban Rural Epidemiology Study (CURES-70) [8].

Beyond these cross-sectional findings, large-scale cohort data provide robust evidence. Analysis of a large Taiwanese database found that individuals with iron deficiency anemia had a 1.52 times higher risk of developing depression and other mental health disorders, with iron treatment notably reducing this likelihood [9]. Experimental and neurobiological studies have further clarified the mechanisms involved, showing that iron deficiency alters hippocampal and striatal neurons and disrupts monoamine synthesis (serotonin, dopamine), underlying anxiety, depression, and cognitive deficits [10]. Finally, a systematic review and meta-analysis of observational studies confirmed that adults with anemia have nearly twice the odds of depression compared to their non-anemic counterparts [11].

Despite growing recognition of this comorbidity, data on the prevalence and correlates of depression among patients with anemia in rural Indian healthcare settings remain limited. In these settings, both anemia and mental health conditions are under-recognized and under-treated. This study aims to estimate the prevalence of depression among patients with anemia attending the Rural Health Training Centre (RHTC) in Kheri Kalan, Faridabad, Haryana. By exploring associated factors, the findings may provide valuable insights for healthcare providers and policymakers to more effectively address the dual burden of anemia and depression.

METHODS

Study Design and Setting

A cross-sectional study was conducted at the RHTC (Amrita School of Medicine), Kheri Kalan, Faridabad, Haryana, India, from October 2024 to February 2025.

Participants

All eligible patients aged >18 years who presented to RHTC with a prior diagnosis of anemia during the study period were invited to participate.

Inclusion criteria: Age>18 years, diagnosed with anemia, and provided written informed consent.

Exclusion criteria: Pregnant or lactating, known psychiatric disorders, bedridden or immobile.

Sample Size and Sampling

Taking the prevalence of unrecognized depression from the study conducted by Kohli et al. [7] as 23.8%, with a two-sided α of 0.05 and a 5% margin of error, the minimum sample size required was calculated as 277. Allowing for a 5% non-response rate, 292 participants were required; to ensure adequate power, 300 individuals were enrolled. Consecutive sampling was used, meaning that every eligible patient was recruited until the target was met.

The sample size was calculated using the formula

N=z1−α/22p(1−p)d2,

where N is the sample size, z1−α/2 is the value of the normal deviate at a 5% significance level (1.96), p is the prevalence of depression in the community (23.8%), and d is the allowable absolute error (5%).

Data Collection Tools

(1) A series of structured questions was administered by trained personnel to gather relevant information: Socio-demographic data: age, sex, education level (illiterate, up to high school, or above high school)

(2) Socioeconomic status based on the BG Prasad scale (2024), which categorizes individuals into 5 classes according to monthly per capita income (in Indian rupees) [12]: I: upper class (≥9131); II: upper middle class (4566-9130); III: middle class (2739-4565); IV: lower middle class (1370-2738); V: lower class (<1370)

(3) Clinical data: Severity of anemia classified by preexisting hemoglobin thresholds per WHO criteria [13]; presence of comorbid conditions (hypertension and diabetes); certain symptoms (tiredness, weakness, drowsiness, and shortness of breath); substance use (alcohol, smoking, tobacco)

(4) Grading of anemia severity according to the WHO classification (in g/dL): Mild: 11.0 to 12.9 (male); 11.0 to 11.9 (female); Moderate: 8.0 to 10.9; Severe: less than 8.0

Depression Assessment

Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), a standardized and validated tool yielding scores ranging from 0 to 27. A score of 10 or more was considered to indicate a positive result for depression. A PHQ-9 cutoff of ≥10 has 88% sensitivity and 88% specificity for major depression [14]. Total scores are categorized as follows: no/mild (≤9); moderate (10-14); moderately severe (15-19); severe (≥20).

Two outcome variables were generated: Depression presence: PHQ-9 score >9 versus ≤9; Depression severity: ordinal categories delineated above.

Statistical Analysis

Data were analyzed using SPSS version 26 (IBM Corp., Armonk, NY, USA). Continuous variables are presented as mean±standard deviation and categorical variables as counts and percentages. The bivariate association between anemia severity and the presence of depression was tested using the chi-square test of association.

Multivariable logistic regression (presence of depression) and proportional-odds ordinal logistic regression (severity of depression) were performed, adjusted for age, sex, education level, socioeconomic status, comorbidities, symptoms, and substance use. p-values of less than 0.05 were considered to indicate statistical significance.

Ethics Statement

Ethical approval for this study was obtained from the institutional ethics committee (AIMS-IEC-BAS-07-24-003). Informed consent was obtained from all participants before data collection. Confidentiality and privacy of individuals were strictly maintained throughout the study.

RESULTS

Participant Characteristics

Overall, 300 participants were included (mean age, 31.6±13.5 years; 89.0% female). Regarding education level, 25.7% of patients were illiterate, 47.3% had education up to high school, and 27.0% had education above high school. Socioeconomic status was distributed as follows: lower class, 1.7%; lower middle class, 23.3%; middle class, 38.7%; upper middle class, 26.0%; and upper class, 10.3%. Anemia severity was mild in 30.7%, moderate in 58.7%, and severe in 10.7% (Table 1). In total, 94 participants (31.3%) screened positive for depression (PHQ-9 >9), with severity classified as no/mild in 68.7%, moderate in 21.7%, moderately severe in 8.3%, and severe in 1.3% (Table 2).

Association Between Anemia Severity and Depression Presence

The proportion of participants with depression increased with anemia severity: 20.7% in mild, 33.5% in moderate, and 50.0% in severe anemia. A chi-square test indicated a significant association (χ²=10.45, degrees of freedom [df]=2, p=0.005) (Figure 1, Table 3).

Multivariable Logistic Regression Analysis of Depression Presence

After adjustment for age, sex, education level, socioeconomic status, symptoms (tiredness, weakness, drowsiness, shortness of breath), comorbidities (hypertension, diabetes), and substance use (smoking, alcohol, tobacco), severe anemia was independently associated with higher odds of depression (odds ratio [OR], 3.02; 95% confidence interval [CI], 1.13 to 8.07; p=0.027), whereas moderate anemia displayed a non-significant trend (OR, 1.77; 95% CI, 0.89 to 3.53; p=0.105). Among clinical covariates, weakness (OR, 3.57; 95% CI, 1.05 to 12.07; p=0.041) and shortness of breath (OR, 2.71; 95% CI, 1.48 to 4.98; p=0.001) were also significantly associated with depression (Table 4). All key assumptions for multivariable logistic regression were met. Variance inflation factors for all model variables ranged from 1.1 to 2.3, well below the conservative threshold of 5.0; thus, multicollinearity among factors was absent. The Box–Tidwell test for linearity of the logit with respect to age was non-significant (p=0.45), supporting the assumption of linearity. The Hosmer–Lemeshow goodness-of-fit test indicated good model fit (χ²₈=7.8, p=0.45), and the model’s discriminative ability was acceptable (area under the receiver operating characteristic curve, 0.78).

Ordinal Logistic Regression Analysis of Depression Severity

In the proportional-odds model adjusting for the same covariates, severe anemia remained significantly associated with greater depression severity (OR, 2.87; 95% CI, 1.14 to 7.27; p=0.026), while moderate anemia did not (OR, 1.64; 95% CI, 0.84 to 3.18; p=0.146). Weakness (OR, 3.59; 95% CI, 1.09 to 11.81; p=0.035) and shortness of breath (OR, 2.63; 95% CI, 1.46 to 4.75; p=0.001) also displayed significant relationships (Table 4). The proportional-odds assumption was met (χ²=12.1, df=14, p=0.60), confirming that a single set of coefficients across outcome thresholds was appropriate and justifying the use of the ordinal logistic regression model. Pseudo R-squared values (Nagelkerke, 0.19; Cox and Snell, 0.14) indicated moderate model fit.

Overall diagnostic testing demonstrated that the regression assumptions were satisfied: multicollinearity was absent, model fit and discrimination were strong, and the proportional-odds assumption held. These results confirm that the logistic and ordinal approaches chosen were appropriate and robust for analyzing the presence and severity of depression.

DISCUSSION

In this cross-sectional study of 300 adults, severe anemia was independently associated with both the presence of depression (adjusted OR, 3.02; 95% CI, 1.13 to 8.07; p=0.027) and greater severity of depression (adjusted OR, 2.87; 95% CI, 1.14 to 7.27; p=0.026), even after adjusting for socio-demographic factors, anemia-related symptoms, comorbid conditions, and substance use. Moderate anemia also exhibited a non-significant trend towards increased risk of depression (adjusted OR, 1.77; 95% CI, 0.89 to 3.53; p=0.105).

Our findings align with those of a recent meta-analysis of observational studies, which reported a pooled association between low hemoglobin levels and adult depression (pooled OR, 1.43) [11]. This meta-analysis included heterogeneous populations and measurement methods; thus, our predominantly female sample (89% female) and use of the validated PHQ-9 instrument [14] may have enhanced sensitivity, leading to detection of a stronger effect.

Biologically, anemia impairs oxygen delivery to the brain and peripheral tissues, leading to symptoms such as fatigue, weakness, drowsiness, and shortness of breath. These symptoms are significantly associated with depression. Iron is a key cofactor for enzymes involved in monoamine neurotransmitter synthesis, including serotonin and dopamine. Studies indicate that iron deficiency can alter hippocampal glucocorticoid receptor signaling, potentially contributing to mood disturbances [15,16]. Our results specifically identified weakness (adjusted OR, 3.57; p=0.041) and shortness of breath (adjusted OR, 2.71; p=0.001) as associated with depression, underscoring the interplay between physiological burden and psychological distress.

From a public health perspective, these findings emphasize the importance of routine depression screening (e.g., using the PHQ-9) in patients with moderate to severe anemia, as well as assessing anemia status in individuals presenting with depressive symptoms. Integrating mental health evaluation into anemia management protocols may enable earlier detection and more coordinated, comprehensive care. Community interventions focused on iron-rich nutrition, fortification, and supplementation could simultaneously reduce anemia prevalence and its associated neuropsychiatric effects.

Strengths of this study include detailed symptom measurement, adjustment for a broad range of covariates, and the use of both binary and ordinal regression analyses to capture depression severity. Limitations include the cross-sectional design (which precludes causal inference), reliance on self-reported PHQ-9 data without clinical diagnostic confirmation, and limited generalizability due to the high proportion of female participants and the non-probability sampling of clinic attendees. Notably, excluding patients with known psychiatric disorders minimized confounding effects, allowing precise characterization of the direct association between anemia and depression. While this approach may yield conservative estimates of depression prevalence, it reinforces the validity of our findings and provides a clear foundation for future research.

Future research should focus on prospective cohort studies to determine whether anemia management reduces depressive symptoms, as well as randomized trials to evaluate integrated treatment models.

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Acknowledgements

None.

Author Contributions

Conceptualization: Shamsi J, Prince. Data curation: Prince. Formal analysis: Prince, Shamsi J. Funding acquisition: None. Methodology: Shamsi J, Singh SV. Project administration: Prince, Urvashi, Sharma R, Shamsi J, Singh SV. Visualization: Shamsi J, Prince, Urvashi, Sharma R. Writing – original draft: Prince, Shamsi J. Writing – review & editing: Prince, Urvashi, Sharma R, Shamsi J, Singh SV.

Figure. 1.

Presence of depression (PHQ-9>9 score) among grades of anemia.

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